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所在平台: Udemy |
课程主页: https://www.udemy.com/course/rnaseq-data-analysis-using-shell-scripting-and-r/
课程评论：没有评论
课程名称：使用Shell脚本和R进行RNA测序数据分析 概述：本课程将教你如何通过Linux命令行执行RNA测序数据分析。课程全面介绍RNA测序数据分析的关键概念和工具，涵盖了差异表达分析和功能注释的必要知识。学生将学习如何预处理原始测序数据，进行质量控制，并将读数比对到参考基因组或转录组上。课程还将讲解使用统计方法进行差异表达分析，并利用R等流行工具可视化结果。你将学习如何进行完整的RNA测序数据分析，包括RNA测序数据的预处理、质量控制分析、差异基因表达分析、基因表达数据的聚类和主成分分析。此外，课程中还会介绍如何下载数据，使用Conda/Anaconda在Mac、Windows或Linux平台上安装生物信息学/IT软件。讲师将引导你在RStudio（R语言的图形用户界面）中执行差异表达分析。 在整个课程中，学生将使用真实世界的数据集，获得热门生物信息学工具和软件包的实际操作经验。课程结束时，学生将对RNA测序数据分析有深入理解，并能够独立执行基因表达数据的分析。该课程特别适合对基因表达分子基础感兴趣的研究人员、科学家和学生，探索RNA测序技术的潜在应用。不需要有生物信息学或编程经验，但建议对分子生物学和遗传学有基本了解。
In this course, you will learn how to perform RNAseq data analysis via linux command line. This course provides a comprehensive introduction to RNAseq data analysis, covering the key concepts and tools needed to perform differential expression analysis and functional annotation of RNAseq data. Students will learn how to preprocess raw sequencing data, perform quality control, and align reads to a reference genome or transcriptome. The course will also cover differential expression analysis using statistical methods and visualisation of results using popular tools such as R. You will learn how to do end-to-end RNAseq data analysis which includes pre-processing of RNAseq data, Quality Control analysis, Differential Gene Expression analysis, Clustering and Principal Component Analysis of the gene expression data. You will also learn how to download data, install the bioinformatics/IT softwares using Conda/Anaconda on Mac, Windows or Linux platforms. I will guide you through performing differential expression analysis on RStudio (graphical user interface for R language).Throughout the course, students will work with real-world datasets and gain hands-on experience with popular bioinformatics tools and software packages. By the end of the course, students will have a thorough understanding of RNAseq data analysis and will be able to perform their own analyses of gene expression data. This course is ideal for researchers, scientists, and students who are interested in understanding the molecular basis of gene expression and exploring the potential applications of RNAseq technology. No prior bioinformatics or programming experience is required, but a basic knowledge of molecular biology and genetics is recommended.
